
import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
				import jax
				import jax.numpy as jnp




@slow
@require_flax
class 		lowercase__    (		unittest.TestCase    ):
	def 	A_							(     self				:  Optional[int]			,  UpperCAmelCase_				:  Union[str, Any]			,  UpperCAmelCase_				:  Optional[Any]				):
						return F'gaussian_noise_s={seed}_shape={"_".join([str(UpperCAmelCase_				) for s in shape]				)}.npy'
	def 	A_							(     self				:  int				):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
	def 	A_							(     self				:  str			,  UpperCAmelCase_				:  str=0			,  UpperCAmelCase_				:  str=(4, 4, 64, 64)			,  UpperCAmelCase_				:  Optional[int]=False				):
						SCREAMING_SNAKE_CASE__						=					jnp.bfloataa if fpaa else jnp.floataa
						SCREAMING_SNAKE_CASE__						=					jnp.array(load_hf_numpy(self.get_file_format(UpperCAmelCase_			,  UpperCAmelCase_				)				)			,  dtype=UpperCAmelCase_				)
						return image
	def 	A_							(     self				:  List[Any]			,  UpperCAmelCase_				:  List[Any]=False			,  UpperCAmelCase_				:  List[str]="CompVis/stable-diffusion-v1-4"				):
						SCREAMING_SNAKE_CASE__						=					jnp.bfloataa if fpaa else jnp.floataa
						SCREAMING_SNAKE_CASE__						=					'bf16' if fpaa else None

						SCREAMING_SNAKE_CASE__						=					FlaxUNetaDConditionModel.from_pretrained(
						    UpperCAmelCase_			,  subfolder='unet'			,  dtype=UpperCAmelCase_			,  revision=UpperCAmelCase_				)
						return model, params
	def 	A_							(     self				:  str			,  UpperCAmelCase_				:  Any=0			,  UpperCAmelCase_				:  str=(4, 77, 768)			,  UpperCAmelCase_				:  List[str]=False				):
						SCREAMING_SNAKE_CASE__						=					jnp.bfloataa if fpaa else jnp.floataa
						SCREAMING_SNAKE_CASE__						=					jnp.array(load_hf_numpy(self.get_file_format(UpperCAmelCase_			,  UpperCAmelCase_				)				)			,  dtype=UpperCAmelCase_				)
						return hidden_states
	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
	        [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
	        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
	        [3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
	        # fmt: on
	    ]				)
	def 	A_							(     self				:  Any			,  UpperCAmelCase_				:  str			,  UpperCAmelCase_				:  Tuple			,  UpperCAmelCase_				:  Optional[int]				):
						SCREAMING_SNAKE_CASE__						=					self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'			,  fpaa=UpperCAmelCase_				)
						SCREAMING_SNAKE_CASE__						=					self.get_latents(UpperCAmelCase_			,  fpaa=UpperCAmelCase_				)
						SCREAMING_SNAKE_CASE__						=					self.get_encoder_hidden_states(UpperCAmelCase_			,  fpaa=UpperCAmelCase_				)

						SCREAMING_SNAKE_CASE__						=					model.apply(
						    {'params': params}			,  UpperCAmelCase_			,  jnp.array(UpperCAmelCase_			,  dtype=jnp.intaa				)			,  encoder_hidden_states=UpperCAmelCase_			,  ).sample

						assert sample.shape == latents.shape

						SCREAMING_SNAKE_CASE__						=					jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())				)			,  dtype=jnp.floataa				)
						SCREAMING_SNAKE_CASE__						=					jnp.array(UpperCAmelCase_			,  dtype=jnp.floataa				)

						# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
						assert jnp.allclose(UpperCAmelCase_			,  UpperCAmelCase_			,  atol=1e-2				)





	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
	        [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
	        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
	        [3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
	        # fmt: on
	    ]				)
	def 	A_							(     self				:  int			,  UpperCAmelCase_				:  Any			,  UpperCAmelCase_				:  Optional[Any]			,  UpperCAmelCase_				:  Optional[int]				):
						SCREAMING_SNAKE_CASE__						=					self.get_unet_model(model_id='stabilityai/stable-diffusion-2'			,  fpaa=UpperCAmelCase_				)
						SCREAMING_SNAKE_CASE__						=					self.get_latents(UpperCAmelCase_			,  shape=(4, 4, 96, 96)			,  fpaa=UpperCAmelCase_				)
						SCREAMING_SNAKE_CASE__						=					self.get_encoder_hidden_states(UpperCAmelCase_			,  shape=(4, 77, 1024)			,  fpaa=UpperCAmelCase_				)

						SCREAMING_SNAKE_CASE__						=					model.apply(
						    {'params': params}			,  UpperCAmelCase_			,  jnp.array(UpperCAmelCase_			,  dtype=jnp.intaa				)			,  encoder_hidden_states=UpperCAmelCase_			,  ).sample

						assert sample.shape == latents.shape

						SCREAMING_SNAKE_CASE__						=					jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())				)			,  dtype=jnp.floataa				)
						SCREAMING_SNAKE_CASE__						=					jnp.array(UpperCAmelCase_			,  dtype=jnp.floataa				)

						# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
						assert jnp.allclose(UpperCAmelCase_			,  UpperCAmelCase_			,  atol=1e-2				)













"""simple docstring"""


from math import loga


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if a < 0:
												raise ValueError('Input value must be a positive integer' )
						elif isinstance(a__     ,      a__ ):
												raise TypeError('Input value must be a \'int\' type' )
						return 0 if (a == 0) else int(loga(a & -a ) )


if __name__ == "__main__":
		import doctest

		doctest.testmod()